08.Rainbow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rainbow: Combining Improvements in Deep Reinforcement Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym, math, glob, random\n",
    "import numpy as np\n",
    "\n",
    "from timeit import default_timer as timer\n",
    "from datetime import timedelta\n",
    "\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from IPython.display import clear_output\n",
    "from matplotlib import pyplot as plt\n",
    "get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "from utils.wrappers import *\n",
    "from utils.ReplayMemory import PrioritizedReplayMemory\n",
    "from networks.layers import NoisyLinear\n",
    "from agents.DQN import Model as DQN_Agent\n",
    "\n",
    "from utils.hyperparameters import Config\n",
    "from utils.plot import plot_all_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = Config()\n",
    "\n",
    "config.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "config.NOISY_NETS = True\n",
    "config.USE_PRIORITY_REPLAY = True\n",
    "\n",
    "\n",
    "#misc agent variables\n",
    "config.GAMMA=0.99\n",
    "config.LR=1e-4\n",
    "\n",
    "#memory\n",
    "config.TARGET_NET_UPDATE_FREQ = 1000\n",
    "config.EXP_REPLAY_SIZE = 100000\n",
    "config.BATCH_SIZE = 32\n",
    "config.PRIORITY_ALPHA=0.6\n",
    "config.PRIORITY_BETA_START=0.4\n",
    "config.PRIORITY_BETA_FRAMES = 100000\n",
    "\n",
    "#epsilon variables\n",
    "config.SIGMA_INIT=0.5\n",
    "\n",
    "#Learning control variables\n",
    "config.LEARN_START = 10000\n",
    "config.MAX_FRAMES=700000\n",
    "config.UPDATE_FREQ = 1\n",
    "\n",
    "#Categorical Params\n",
    "config.ATOMS = 51\n",
    "config.V_MAX = 10\n",
    "config.V_MIN = -10\n",
    "\n",
    "#Multi-step returns\n",
    "config.N_STEPS = 3\n",
    "\n",
    "#data logging parameters\n",
    "config.ACTION_SELECTION_COUNT_FREQUENCY = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CategoricalDuelingDQN(nn.Module):\n",
    "    def __init__(self, input_shape, num_actions, sigma_init=0.5, atoms=51):\n",
    "        super(CategoricalDuelingDQN, self).__init__()\n",
    "        \n",
    "        self.input_shape = input_shape\n",
    "        self.num_actions = num_actions\n",
    "        self.atoms = atoms\n",
    "\n",
    "        self.conv1 = nn.Conv2d(self.input_shape[0], 32, kernel_size=8, stride=4)\n",
    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)\n",
    "        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)\n",
    "\n",
    "        self.adv1 = NoisyLinear(self.feature_size(), 512, sigma_init)\n",
    "        self.adv2 = NoisyLinear(512, self.num_actions*self.atoms, sigma_init)\n",
    "\n",
    "        self.val1 = NoisyLinear(self.feature_size(), 512, sigma_init)\n",
    "        self.val2 = NoisyLinear(512, 1*self.atoms, sigma_init)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.relu(self.conv3(x))\n",
    "        x = x.view(x.size(0), -1)\n",
    "        adv = F.relu(self.adv1(x))\n",
    "        adv = self.adv2(adv).view(-1, self.num_actions, self.atoms)\n",
    "\n",
    "        val = F.relu(self.val1(x))\n",
    "        val = self.val2(val).view(-1, 1, self.atoms)\n",
    "\n",
    "        final = val + adv - adv.mean(dim=1).view(-1, 1, self.atoms)\n",
    "\n",
    "        return F.softmax(final, dim=2)\n",
    "    \n",
    "    def feature_size(self):\n",
    "        return self.conv3(self.conv2(self.conv1(torch.zeros(1, *self.input_shape)))).view(1, -1).size(1)\n",
    "\n",
    "    def sample_noise(self):\n",
    "        self.adv1.sample_noise()\n",
    "        self.adv2.sample_noise()\n",
    "        self.val1.sample_noise()\n",
    "        self.val2.sample_noise()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model(DQN_Agent):\n",
    "    def __init__(self, static_policy=False, env=None, config=None, log_dir='/tmp/gym'):\n",
    "        self.atoms=config.ATOMS\n",
    "        self.v_max=config.V_MAX\n",
    "        self.v_min=config.V_MIN\n",
    "        self.supports = torch.linspace(self.v_min, self.v_max, self.atoms).view(1, 1, self.atoms).to(config.device)\n",
    "        self.delta = (self.v_max - self.v_min) / (self.atoms - 1)\n",
    "\n",
    "        super(Model, self).__init__(static_policy, env, config, log_dir=log_dir)\n",
    "\n",
    "        self.nsteps=max(self.nsteps,3)\n",
    "    \n",
    "    def declare_networks(self):\n",
    "        self.model = CategoricalDuelingDQN(self.env.observation_space.shape, self.env.action_space.n, sigma_init=self.sigma_init, atoms=self.atoms)\n",
    "        self.target_model = CategoricalDuelingDQN(self.env.observation_space.shape, self.env.action_space.n, sigma_init=self.sigma_init, atoms=self.atoms)\n",
    "\n",
    "    def declare_memory(self):\n",
    "        self.memory = PrioritizedReplayMemory(self.experience_replay_size, self.priority_alpha, self.priority_beta_start, self.priority_beta_frames)\n",
    "\n",
    "    def projection_distribution(self, batch_vars):\n",
    "        batch_state, batch_action, batch_reward, non_final_next_states, non_final_mask, empty_next_state_values, indices, weights = batch_vars\n",
    "\n",
    "        with torch.no_grad():\n",
    "            max_next_dist = torch.zeros((self.batch_size, 1, self.atoms), device=self.device, dtype=torch.float) + 1./self.atoms\n",
    "            if not empty_next_state_values:\n",
    "                max_next_action = self.get_max_next_state_action(non_final_next_states)\n",
    "                self.target_model.sample_noise()\n",
    "                max_next_dist[non_final_mask] = self.target_model(non_final_next_states).gather(1, max_next_action)\n",
    "                max_next_dist = max_next_dist.squeeze()\n",
    "\n",
    "\n",
    "            Tz = batch_reward.view(-1, 1) + (self.gamma**self.nsteps)*self.supports.view(1, -1) * non_final_mask.to(torch.float).view(-1, 1)\n",
    "            Tz = Tz.clamp(self.v_min, self.v_max)\n",
    "            b = (Tz - self.v_min) / self.delta\n",
    "            l = b.floor().to(torch.int64)\n",
    "            u = b.ceil().to(torch.int64)\n",
    "            l[(u > 0) * (l == u)] -= 1\n",
    "            u[(l < (self.atoms - 1)) * (l == u)] += 1\n",
    "            \n",
    "\n",
    "            offset = torch.linspace(0, (self.batch_size - 1) * self.atoms, self.batch_size).unsqueeze(dim=1).expand(self.batch_size, self.atoms).to(batch_action)\n",
    "            m = batch_state.new_zeros(self.batch_size, self.atoms)\n",
    "            m.view(-1).index_add_(0, (l + offset).view(-1), (max_next_dist * (u.float() - b)).view(-1))  # m_l = m_l + p(s_t+n, a*)(u - b)\n",
    "            m.view(-1).index_add_(0, (u + offset).view(-1), (max_next_dist * (b - l.float())).view(-1))  # m_u = m_u + p(s_t+n, a*)(b - l)\n",
    "\n",
    "        return m\n",
    "    \n",
    "    def compute_loss(self, batch_vars):\n",
    "        batch_state, batch_action, batch_reward, non_final_next_states, non_final_mask, empty_next_state_values, indices, weights = batch_vars\n",
    "\n",
    "        batch_action = batch_action.unsqueeze(dim=-1).expand(-1, -1, self.atoms)\n",
    "        batch_reward = batch_reward.view(-1, 1, 1)\n",
    "\n",
    "        #estimate\n",
    "        self.model.sample_noise()\n",
    "        current_dist = self.model(batch_state).gather(1, batch_action).squeeze()\n",
    "\n",
    "        target_prob = self.projection_distribution(batch_vars)\n",
    "          \n",
    "        loss = -(target_prob * current_dist.log()).sum(-1)\n",
    "        self.memory.update_priorities(indices, loss.detach().squeeze().abs().cpu().numpy().tolist())\n",
    "        loss = loss * weights\n",
    "        loss = loss.mean()\n",
    "\n",
    "        return loss\n",
    "\n",
    "    def get_action(self, s):\n",
    "        with torch.no_grad():\n",
    "            X = torch.tensor([s], device=self.device, dtype=torch.float)\n",
    "            self.model.sample_noise()\n",
    "            a = self.model(X) * self.supports\n",
    "            a = a.sum(dim=2).max(1)[1].view(1, 1)\n",
    "            return a.item()\n",
    "\n",
    "    def get_max_next_state_action(self, next_states):\n",
    "        next_dist = self.model(next_states) * self.supports\n",
    "        return next_dist.sum(dim=2).max(1)[1].view(next_states.size(0), 1, 1).expand(-1, -1, self.atoms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start=timer()\n",
    "\n",
    "log_dir = \"/tmp/gym/\"\n",
    "try:\n",
    "    os.makedirs(log_dir)\n",
    "except OSError:\n",
    "    files = glob.glob(os.path.join(log_dir, '*.monitor.csv')) \\\n",
    "        + glob.glob(os.path.join(log_dir, '*td.csv')) \\\n",
    "        + glob.glob(os.path.join(log_dir, '*sig_param_mag.csv')) \\\n",
    "        + glob.glob(os.path.join(log_dir, '*action_log.csv'))\n",
    "    for f in files:\n",
    "        os.remove(f)\n",
    "\n",
    "env_id = \"PongNoFrameskip-v4\"\n",
    "env    = make_atari(env_id)\n",
    "env    = bench.Monitor(env, os.path.join(log_dir, env_id))\n",
    "env    = wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=True)\n",
    "env    = WrapPyTorch(env)\n",
    "model  = Model(env=env, config=config, log_dir=log_dir)\n",
    "\n",
    "episode_reward = 0\n",
    "\n",
    "observation = env.reset()\n",
    "for frame_idx in range(1, config.MAX_FRAMES + 1):\n",
    "    action = model.get_action(observation)\n",
    "    model.save_action(action, frame_idx) #log action selection\n",
    "\n",
    "    prev_observation=observation\n",
    "    observation, reward, done, _ = env.step(action)\n",
    "    observation = None if done else observation\n",
    "\n",
    "    model.update(prev_observation, action, reward, observation, frame_idx)\n",
    "    episode_reward += reward\n",
    "\n",
    "    if done:\n",
    "        model.finish_nstep()\n",
    "        model.reset_hx()\n",
    "        observation = env.reset()\n",
    "        model.save_reward(episode_reward)\n",
    "        episode_reward = 0\n",
    "\n",
    "    if frame_idx % 10000 == 0:\n",
    "        model.save_w()\n",
    "        try:\n",
    "            clear_output(True)\n",
    "            plot_all_data(log_dir, env_id, 'RainbowDQN', config.MAX_FRAMES, bin_size=(10, 100, 100, 1), smooth=1, time=timedelta(seconds=int(timer()-start)), ipynb=True)\n",
    "        except IOError:\n",
    "            pass\n",
    "\n",
    "model.save_w()\n",
    "env.close()\n",
    "plot_all_data(log_dir, env_id, 'RainbowDQN', config.MAX_FRAMES, bin_size=(10, 100, 100, 1), smooth=1, time=timedelta(seconds=int(timer()-start)), ipynb=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}